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public static void sort(int[] a) { // POSTCONDITION: a[0] <= a[1] <= ... <= a[a.length-1]; int min = min(a); int max = max(a); int n = a.length; Bucket[] bucket = new Bucket[n]; // step 1 for (int j=0; j<n; j++) { bucket[j] = new Bucket(); } for (int i=0; i<n; i++) { // step 2 int j = n*(a[i] - min)/(max + 1 - min); bucket[j].add(a[i]); // step 3 } int i=0; for (int j=0; j<n; j++) { Bucket bj=bucket[j]; bj.sort(); // step 4 for (int k=0; k<bj.size(); k++) { // step 5 a[i++] = bj.get(k); } } } private static int min(int[] a) { int min = a[0]; for (int ai: a) { if (ai < min) { min = ai; } } return min; } private static int max(int[] a) { int max = a[0]; for (int ai: a) { if (ai > max) { max = ai; } } return max; }

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hdn) =

f ( n )=

14.1 If an O(n2) algorithm (e.g., the bubble sort, the selection sort, or the insertion sort) takes 3.1 milliseconds to run on an array of 200 elements, how long would you expect it to take to run on a similar array of: a. 400 elements b. 40,000 elements If an O(n lg n) algorithm (e.g., the merge sort, the quick sort, or the heap sort) takes 3.1 milliseconds to run on an array of 200 elements, how long would you expect it to take to run on a similar array of 40,000 elements The insertion sort runs in linear time on an array that is already sorted. How does it do on an array that is sorted in reverse order

This program requires the implementation of this interface:

* h ( n )+ e d n ) * h d n )

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(b) If the order of the cascade is reversed. we have the following network:

Figure 12.9 on page 236 shows the binary search tree that is built by inserting the input sequence 44, 22, 77, 55, 99, 88, 33.

public interface Bucket { public void add(int x); public int get(int k); public int size(); } // appends x to end of bucket // returns element k from bucket // returns the number of elements

The variance of the round-off noise due to e l ( n ) is the same as in part (a),but because the unit sample response of the second system in the cascade is now

h; (n) ( i ) n u ( n ) =

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If a binary search tree is balanced, it allows for very efficient searching. As with the binary search, it takes O(lgn) steps to find an element in a balanced binary search tree. But without further restrictions, a binary search tree may grow to be very unbalanced. The worst case is when the elements are inserted in sorted order. In that case the tree degrades to a linear list, thereby making the search algorithm an O(n) sequential search. EXAMPLE 12.6 An Unbalanced Binary Search Tree

For example:

a = 1.82860: :

+ 1.33330:

This is the same input data as in Example 12.5, but in a different order: 99, 22, 88, 33, 77, 55, 44. The resulting binary search tree is shown in Figure 12.10 on page 236. This shows that the same input in different order produces a different tree. But more important, it shows that it is not unlikely for the binary search tree to be linear, or nearly linear.

43 44 45 46 47

= 3.16190;

H (z) =

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